Appendix C
Observations, Consequences, and Opportunities: The Site Visits of the Committee

Table C.1, which summarizes the committee’s observations from the site visits, is structured as follows.

  • Column 1—Observations (what committee members saw during the site visits). Under each observation are listed one or more de-identified data points. The high-level observation is the abstraction for those data points. The committee grouped the observations into six categories:

    • Category 1. The medical record itself—the display, the application, the paper; in general, what the user interacts with directly.

    • Category 2. The health care delivery process—the workflow, what happens when, who does it, how decisions are made, how communication occurs.

    • Category 3. Health care professionals—what they are like, how they react to IT, and so on.

    • Category 4. IT infrastructure and management—the underlying computing substrate and how it is managed.

    • Category 5. Data capture and flow—how data are gathered, recorded, and passed among systems, records, and people.



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Appendix C Observations, Consequences, and Opportunities: The Site Visits of the Committee Table C.1, which summarizes the committee’s observations from the site visits, is structured as follows. • olumn 1—Observations (what committee members saw during C the site visits). Under each observation are listed one or more de- identified data points. The high-level observation is the abstraction for those data points. The committee grouped the observations into six categories: —Category 1. The medical record itself—the display, the applica- tion, the paper; in general, what the user interacts with directly. —Category 2. The health care delivery process—the workflow, what happens when, who does it, how decisions are made, how communication occurs. —Category 3. Health care professionals—what they are like, how they react to IT, and so on. —Category 4. IT infrastructure and management—the underlying computing substrate and how it is managed. —Category 5. Data capture and flow—how data are gathered, recorded, and passed among systems, records, and people. 

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 COMPUTATIONAL TECHNOLOGY FOR EFFECTIVE HEALTH CARE —Category 6. Change in a sociotechnical system—how to create envi- ronments that facilitate large-scale change. • olumn 2—Consequences (why the observations matter). For C each observation, the committee infers one or more consequences. That is, why do we care about the observation in question? How might it affect health care delivery? • olumn 3—Opportunities for Action (what we can do about the C consequences). Every observation-consequence pair should pro- vide one or more opportunities for action. Solutions known today but not yet implemented are indicated by an “S” (for short-term) in Column 3; challenges for research, where solutions are not known today, are indicated by an “R” (for research) in Column 3. In Table C.1, the notation CxOy is used. Cx refers to Category x of the committee’s observations as grouped in the table (which lists six categories of observations), and Oy refers to a particular observation as numbered in the table (which includes a total of 25 observations).

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 APPENDIX C TABLE C.1 Committee’s Observations from Its Site Visits Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Do About Ita Saw Matter Category 1. The Medical Record Itself • Synthesis depends • Techniques to 1 Patient records are fragmented on intra-team synthesize and • Computer-based conversation summarize information • Problem recognition and paper records about the patient in co-exist is left to chance and across systems • Computer records • Team members with drill-downs for are divided among waste time getting detail (S/R) • Mechanisms to focus task-specific information in the transaction- form they want to on a constellation of processing systems use related factors (S/R) • Users have to know • Single search box that where to look returns all appropriate • Individual information in the manually annotated appropriate format (R) • Alerts to problems or work lists are the norm trends for investigation (S/R) • “Virtual patient” displays leveraging biological and disease models to reduce multiple data inputs to intelligent summaries of key human systems (R) • Important • Design reflecting 2 Clinical user interfaces mimic their paper information and human and safety predecessors trends are easily factors (S) • The flow sheet is • Automatic capture and overlooked • Cognitive burden the predominant use of context (what, display construct of absorbing the who, when. . .) (S) • No standardization • Techniques to information detracts of location of from thinking represent and capture information or use about what the data at multiple levels of symbols and information means of abstraction (Care— color plan, order, charting; • Font size is data—raw signal, challenging concept derived from the signal; biology) (S/R) continued

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 COMPUTATIONAL TECHNOLOGY FOR EFFECTIVE HEALTH CARE TABLE C.1 Continued Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Do About Ita Saw Matter Category 1. The Medical Record Itself (continued) • Missed opportunity • See Category 5, 3 Systems are used most often to document for decision or observation 19 (C5O19) what has been done, workflow support • Variable frequently hours after the fact completeness and accuracy • Redundant work • Lost opportunity • Peer-to-peer and social 4 Support for evidence- based medicine and to provide patient- networking techniques computer-based advice specific decision for development is rare support of guidelines and decision support content (S/R) • Mass customization techniques for practice guidelines (modules) (R) • Computable knowledge structures and models (R) Category 2. The Health Care Delivery Process • Reactive care • Dynamically 5 High complexity • Handoff errors and coordination computable models to • Redundant care requirements of care represent plan for care, • Within teams workflow, escalation, • Across teams and and so on (R) services within settings • Across settings

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 APPENDIX C TABLE C.1 Continued Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Do About Ita Saw Matter Category 2. The Health Care Delivery Process (continued) • No clear thinking • Scripting languages for 6 Non-transparent workflow about overall decision and workflow • Clinical roles and workflows, support content (S/R) • Uniform provider ID responsibilities are process design, not explicit and efficiency and (S) • Scheduling is • Explicit team roles and handoff errors • Unpredictable negotiated and escalation paths (S/R) • Capabilities for manual escalation and • Care processes response context-aware efficient steps and outcomes scheduling (S/R) are rarely documented in machine-readable manner • See observations 5 • See observations 5 and 7 Work is frequently interrupted with gaps and 6 (C2O5, C2O6) 6 (C2O5, C2O6) between steps and manual handoffs at seams of the process • See observations 5 • See observations 5 and 8 Shift of care from inpatient, to and 6 (C2O5, C2O6) 6 (C2O5, C2O6) • Support for varying outpatient, home, patients, families cultures and education (R) • Low voluntary • Instrumented process 9 Errors and near misses are frequent and use reporting that limits to track steps (S/R) • Automated of data to identify proactive use of patterns is rare near misses for surveillance for system correction potential problems (S/R) • Difficulty deciding • 10 Clinical research Computable models activities not well what to charge to of research plan, integrated into ongoing whom for research or workflow, researcher clinical care care roles, etc. (S/R) • Barriers to subject • Data exchange enrollment between care and • Duplication of research research systems (S/R) • and care processes De-identification • Limited learning from algorithms (S/R) routine practice continued

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 COMPUTATIONAL TECHNOLOGY FOR EFFECTIVE HEALTH CARE TABLE C.1 Continued Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Do About Ita Saw Matter Category 3. Health Care Professionals • Time is money • See Category 5, 11 Clinical users choose • Each second added speed over all else observation 19 (C5O19) to the time to write each prescription in the United States adds 470 physician full-time equivalents • Inefficient • Design system 12 Clinical users do not have a consistent workflow modules for use in • Incomplete or understanding of the production (operation) purpose of a system or inaccurate data and simulation the functionality of the entry (training) (S) • Misinterpretation of user interface information • System work- arounds • Health • Educate health 13 Health professionals’ understanding of professionals do not professionals in how IT might help is know what to ask systems approaches • Imbed informatics limited for • Health experts in clinical professionals do teams (as is done with not know how to pharmacists) • Expand informatics test whether an IT intervention will training programs solve their problem in their setting

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 APPENDIX C TABLE C.1 Continued Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Do About Ita Saw Matter Category 4. IT Infrastructure and Management • Rigid workflow 14 Legacy systems are Architectures to permit predominant in an era of rapid holistic management • Each is handled change of patient information • Semantic meaning as a separate and decision support implementation of clinical content information across (set-up, profiles, is not explicit information systems • Data are not • Decouple infrastructure, management of decision support easily shared transaction processing, content, etc.) within or across data aggregation, and • Implementation organizations decision/workflow • Clinical best focuses on the support (S) • Wrap purchased technology, not on practice and enabling process decision support applications as Web and role changes content are not services (S) • Management of • Leverage ontology and easily shared change holds all document architectures units supported (S) • Use open-source by a system to the implementation techniques for rate of the slowest infrastructure layer (S) • Develop utility member • Data flow among approaches to “operating an organization’s system on demand” systems is very (mass virtualization) (S) limited • Does not support a • See Category 2, 15 Centralization of management dynamic learning observations 5 and 6 and reduction in health care system (C2O5, C2O6) • See observation 14 the number of that can adapt information systems to accommodate (C4O14) is the predominant local needs and method for capabilities standardization continued

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00 COMPUTATIONAL TECHNOLOGY FOR EFFECTIVE HEALTH CARE TABLE C.1 Continued Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Do About Ita Saw Matter Category 4. IT Infrastructure and Management (continued) • Requires • See observation 14 16 Implementation time lines are long and investment far in (C4O14) course changes are advance of benefit • Inconsistent with expensive • Actual president’s goal implementation for electronic time lines for medical records by enterprise-wide 2014 functionality commonly exceed a decade • New systems are being implemented while the previous generations are still being rolled out • Neither is effective • Techniques to 17 Security and privacy compete authenticate a patient to with workflow his/her record (S/R) • Techniques to loosely optimization couple the individual and his/her identities (S/R) • Architectures that enable confidentiality by limiting access according to need to know while supporting transparency in authorization (S/R) • Work-arounds • Approaches that balance 18 Response times • Redundant are variable (from local caching of data subsecond to processes with timeliness of data • Flying blind minutes) and long (S/R) down-times occur (clinical systems down for >24 hours and equipment down for weeks)

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0 APPENDIX C TABLE C.1 Continued Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Do About Ita Saw Matter Category 5. Data Capture and Flow • More time spent • Redesign roles, 19 Data capture/data entry are commonly entering data than process, and manual using data technology to capture • Variable data at the source as completeness and data are created (S/R) • Self-documenting accuracy • Loss of opportunity sensor-rich for decision and environments workflow support (multimedia) (S/R) • See Category 1, observation 2 • Systems intended to • Design reflecting 20 User interfaces do not reflect human factors reduce error create human and safety and safety design new errors factors (S) • Improperly structured pull- down lists • Inconsistent use of location, symbol, and color • Inefficient charting • Mechanism for 21 Biomedical devices are poorly integrated in and intra-team positively identifying every location conflict relationship of device • Inaccurate charting to patient and to use (errors of omission (e.g., drip composition) and inappropriate (S) • Handle a physician’s copying) • Unsafe (5 rights drip order (order for errors) substance, titration parameter), the current setting (nurse response to order), and amount actually administered (charting) as three related but separate concepts (S) continued

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0 COMPUTATIONAL TECHNOLOGY FOR EFFECTIVE HEALTH CARE TABLE C.1 Continued Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Do About Ita Saw Matter Category 5. Data Capture and Flow (continued) • Defeats safety • Limit use to 22 Implementation of positive identification objective subprocesses where the technology is technology is adequate problematic for the workflow (S) • Gaps in the • Measure and chain of positive systematically identification eliminate work- • Work-arounds arounds (S) • Find better technology are common because of missing workflow matches or mismatched (S/R) information • Portable devices are task-specific (different device for lab specimen and medication administration) • Unit doses of medication are not manufactured with computer-readable tags • Lack of • Interfaces that enable 23 Semantic interoperability is interoperability entry of data in almost non-existent limits data and flexible ways, but that knowledge reuse guide the user into using common fields and terminologies in a non-obtrusive fashion (S/R) • Methods to reconcile multiple references to the same real-world entities (e.g., different ways of referring to penicillin) (S/R) • Mechanisms for mining data to discover emerging patterns in data (S/R)

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0 APPENDIX C TABLE C.1 Continued Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Do About Ita Saw Matter Category 6. Change in a Sociotechnical System • Inconsistent use • Focus on the desired 24 Most systems are partially or poorly and work-arounds outcomes instead of or incompletely increase error the technology (S/R) • Benefits are integrated into practice significantly less than anticipated • Reduced investment • Limited innovation • Management that 25 Innovation requires locally adaptable and standardization encourages initiation systems but of improvements by interoperability health professionals (S) • Technology and and evidence-based medicine require more processes that allow standardization local innovation and flexibility but foster collaboration and learning at a national scale (R) aR, solutions still to be discovered (research); S, solutions known today but not imple- mented (short term).

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